Computational and Spectroscopic Tools for the Detection of Bond Covalency in Pu(IV) Materials
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Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas. Here, by leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance. This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies. As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering.
Quantum computing is emerging as a remarkable technology that offers the possibility of achieving major scientific breakthroughs in many areas. By leveraging the unique features of quantum mechanics, quantum computers may be instrumental in advancing many areas, including science, energy, defense, medicine, and finance. This includes solving complex problems whose solution lies well beyond the capacity of contemporary and even future supercomputers that are based on conventional computing technologies. As a foundation for future generations of computing and information processing, quantum computing represents an exciting area for developing new ideas in computer science and computational engineering.
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Common and community software packages, such as ROOT, Geant4 and event generators have been a key part of the LHC's success so far and continued development and optimisation will be critical in the future. The challenges are driven by an ambitious physics programme, notably the LHC accelerator upgrade to high-luminosity, HL-LHC, and the corresponding detector upgrades of ATLAS and CMS. In this document we address the issues for software that is used in multiple experiments (usually even more widely than ATLAS and CMS) and maintained by teams of developers who are either not linked to a particular experiment or who contribute to common software within the context of their experiment activity. We also give space to general considerations for future software and projects that tackle upcoming challenges, no matter who writes it, which is an area where community convergence on best practice is extremely useful.
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The goal of this project is to develop integrated, state-of-the-art, computational modeling tools based on an integrated computational materials engineering (ICME) methodology that are critically needed to enable structural carbon fiber (CF) applications in automobiles. These tools are designed to predict the manufacturing and structural performance of CF composites, including stochastic effects. During the first phase of the project, the manufacturing and structural performance tools, including a stochastic driver, were developed, calibrated, and validated against coupon and component level test results. For this project, from the manufacturing side, the development efforts were focused on high-pressure resin transfer molding (HP-RTM), a potentially game-changing manufacturing technology that is capable of reaching the 3-5-minute cycle time that is necessary for volume manufacturing of composites. On the structural performance side, the crashworthiness of CF structures was studied. Accordingly, computational tools were developed and validated. The resulting differences between the numerical predictions and the experimental results in the first phase of the project were required to be less than 15%. During the second phase of the project, the manufacturing and performance tools were integrated by mapping the manufacturing outcomes (e.g., fiber angles, residual stresses, degree of cure, and defects) into the structural models. Also, using the tools developed in this project, an automotive assembly currently manufactured in steel (2016 GM Malibu) was redesigned to be manufactured using advanced CF composites with the objective of comparing the performance of the CF assembly with the developed ICME model predictions. The weight, performance and cost of the CF assembly were compared with the baseline steel assembly as well. The CF underbody design was shown to be 30% lighter than the corresponding steel design. The CF underbody assembly withstood the side pole impact with less than half the intrusion for the steel assembly. Excellent correlations within 10% for both peak impact load and intrusion were observed between the numerical predictions and experimental results. Based on the cost models developed in the project, the cost increase per Kg saved was determined to be $22. The project team has prepared a total of 28 publications in various journals and conferences and broadly communicated the project results for the benefit of the composite industry. The project team has also prepared a total of 10 patent applications that were submitted to the US Patent and Trademark office.
Computer-aided tools have been successfully developed and used in different segments of the entire process development cycle. For example, there are established tools for chemical process design, simulation, heat exchanger network synthesis and many more. However, one needs an integrated platform to perform process synthesis, design and analysis of chemical processes in a systematic manner. Also, in order to obtain a truly innovative and sustainable solution, it is necessary to search the entire feasible search space of alternatives and select the best process alternative, which is not very easy and will be time consuming. Therefore, it is beneficial to utilize computer-aided methods and tools to enumerate, analyze and determine within the design space, the more sustainable processes. This work highlights the development of a computer aided methodology and corresponding integrated platform for fast, reliable and consistent generation of process flowsheets. As a result, this paper gives an overview of the key concepts and the method is presented along with details of the developed computer-aided tool.
The High Performance Computing Innovation Center (HPCIC) aims to ease the transition for developers to use open source software provided by the lab. HPCIC Developer Tools is a collection of software, containers, cloud configurations, and associated documentation that make it easy to deploy tutorials or small apps to demonstrate lab-developed software. For example, building a tutorial container that includes lab software and interactive interfaces; a command line or web-based tool that accepts user preferences for the tutorial; supporting tools and software development kits (SDKs) for developer interactions or productivity in different languages embraced by the larger developer community such as Go, Rust, and Python; and automation in version control to support continued update of software and associated resources. These tools are best developed in an open source environment such as GitHub, not only to champion the lab's open source software, but for purposes of branding and demonstrating the lab's leadership in open source. Such an effort that brings in more developers to use and contribute to lab software can further improve the quality of the software, and developer experience at the lab.
Computer-aided manufacturing (CAM) techniques for hybrid manufacturing have led to new application areas in the manufacturing industry. In the tooling industry, cooling channels are used to enable specific heating and cooling cycles to improve the performance of the process. These internal cooling channels have been designed with limited manufacturing processes in mind, so, until recently, they were often straight in shape for cross-drilling operations and manufactured from a cast billet. To show a novel application of this common technology, a tool with integrated conformal cooling channels was manufactured using hybrid manufacturing (blown-powder DED and CNC machining) techniques. The computer-aided manufacturing strategy used, and the lessons learned are presented and discussed to enable future work in this industrial application space.
This paper overviews recent advances in developing novel alloy design concepts of creep-resistant, alumina-forming Fe-base alloys, including both ferritic and austenitic steels, for high-temperature structural applications in fossil-fired power generation systems. Protective, external alumina-scales offer improved oxidation resistance compared to chromia-scales in steam-containing environments at elevated temperatures. Alloy design utilizes computational thermodynamic tools with compositional guidelines based on experimental results accumulated in the last decade, along with design and control of the second-phase precipitates to maximize high-temperature strengths. The alloys developed to date, including ferritic (Fe-Cr-Al-Nb-W base) and austenitic (Fe-Cr-Ni-Al-Nb base) alloys, successfully incorporated the balanced properties of steam/water vapor-oxidation and/or ash-corrosion resistance and improved creep strength. Development of cast alumina-forming austenitic (AFA) stainless steel alloys is also in progress with successful improvement of higher temperature capability targeting up to ~1100°C. Current alloy design approach and developmental efforts with guidance of computational tools were found to be beneficial for further development of the new heat resistant steel alloys for various extreme environments.
The U.S. Department of Energy's Nuclear Energy Advanced Modeling and Simulation (NEAMS) program has been developing computational analysis tools for nuclear fuel simulation. These codes include BISON and Marmot for investigation of fuel behavior at the continuum and microstructural scales, respectively. In 2014, NEAMS introduced what it calls High Impact Problems (HIPs) into its development plan. These HIPs are intended to make significant advancements in a particular area of nuclear power research in a short period of time (3 years or less). NEAMS has chosen an accident tolerant fuel (ATF) project, which emphasizes utilizing BISON and Marmot to model proposed materials, as its first HIP. Since the events that occurred at the Fukushima-Daiichi nuclear power plant in 2011, the U.S. Department of Energy's Office of Nuclear Energy accelerated research in nuclear fuel concepts with enhanced accident tolerance as part of its Fuel Cycle Research and Development (FCRD) Advanced Fuels Campaign (AFC). Accident tolerant fuels are expected to perform at least as well as conventional fuel during normal conditions and provide significantly increased response time in the event of an accident. Given the many unknowns associated with proposed ATF concepts, the research and development being undertaken by AFC in this area is a challenge, particularly considering the desire to have a lead test rod or assembly in a commercial plant by 2022. With the AFC as its primary stakeholder, the NEAMS ATF HIP is developing material models (using a multi-scale approach) for multiple candidate materials. Through a variety of analyses employing these models, the ATF HIP is providing insight and support to the AFC as it strives to understand these materials and prioritize further development. Use of computational tools such as BISON and Marmot are a vital component of the AFC's strategy since experimental data will not be available broadly and quickly enough to understand the proposed materials fully by the time the AFC must make further development decisions. This summary reviews the overall approach of the NEAMS ATF HIP. First, we give an overview of the current materials chosen for investigation. We also review the overall multi-scale approach to model development. We briefly review the work underway at multiple national laboratories to develop mechanistic engineering scale models based on lower length scale insight. Note that coordinated work is also underway at multiple universities, but those efforts are not detailed here. The NEAMS ATF HIP is assisting the AFC in assessing two accident tolerant materials, uranium silicide fuel and FeCrAl cladding. A multi-scale material model development approach is being undertaken to formulate the models necessary to investigate the materials over a range of normal and off-normal scenarios. This approach includes molecular dynamics, density functional theory, mesoscale modeling, and engineering scale modeling. Sensitivity studies are underway to understand the most critical scenarios and material model input parameters. The information generated is being given to the AFC for their use in selecting promising ATF concepts. (authors)
An electric power system or power grid is optimized using a computer-implemented tool the represents in computer memory the optimization function and at least one constraint, which the processor operates upon using a linear programming solver algorithm. The constraints are represented in memory as data structures that include both real and reactive power terms, corresponding to at least one of a power flow model and a transmission line model. The transmission line model is represented using a piecewise linear representation. The power flow model may also include for each node in the power system a real power loss term representing transmission line loss allocated to that node.
Computer System Security: The software is used to perform remote data integrity checking without relying on a single third party. In order to remove the trusted third party our tool uses distributed ledger (Blockchain) technology. The ledger is used to store evidences (in the form of hash values). These values are used as an input in the integrity checking process.
A computational and experimental strategy assesses the immunogenicity risk of bioengineered therapeutic proteins.